Language & Generation · learning vertical

Track 01 · Language & Generation Novice · start here ~7 min

What is generative AI?

Most older AI tells things apart — spam or not, cat or dog. Generative AI does something new: it creates content — text, images, audio, code — that resembles what it learned from. Learn the difference, the modalities, and how it actually generates, right here on the page.

Module progress

01Two jobs: telling apart vs. creating

Picture two jobs at a bakery: one worker sorts pastries into boxes labeled "croissant" or "muffin," while the other actually bakes a brand-new pastry from a recipe. Older AI mostly does the first job — sorting — and that sorting kind is called discriminative AI: it takes an input and predicts a label (is this email spam or not? is this photo a cat or a dog?). Generative AI goes the other way: from a prompt, it produces brand-new content — a paragraph, an image, a snippet of audio or code — that resembles the data it learned from. Flip between the two below.

ExploreFlip between the two kinds of AI

Input → label

what it does

Discriminative AI sorts an input into a category. Example: given an email, it answers "spam" or "not spam."

Discriminative answers a question about existing data: which category does this belong to?
Generative answers a different question: what is a plausible new example that fits the pattern?
Both learn from data — they just use what they learn for opposite purposes.

02What it can create: the modalities

"Content" covers a lot. Generative AI works across several modalities — different kinds of output, often powered by different model designs under the hood. Switch between them below.

ExploreSwitch modality

Text — written language

Large language models (LLMs) generate sentences, summaries, answers, and translations by predicting plausible next words. This is the modality behind most chat assistants.

Prompt: "Explain photosynthesis to a 10-year-old."

Output: a short, plain-language paragraph (illustrative)

Image — pictures from a description

Image generators — often built on diffusion models — turn a text description into a new picture, starting from random noise and refining it toward the prompt.

Prompt: "A watercolor fox in a snowy forest."

Output: a newly drawn image (illustrative)

Audio — voice, speech & music

Audio models synthesize spoken words from text, clone or create voices, and compose music. The output is generated sound, not a clip pulled from a library.

Prompt: "Read this script in a calm narrator voice."

Output: a generated audio track (illustrative)

Code — programs & snippets

Code models write functions, fix bugs, and explain programs in many languages. Because code is just structured text, LLMs handle it as another kind of language.

Prompt: "Write a function that reverses a string."

Output: a short code snippet (illustrative)

03How it actually generates

Generative AI doesn't copy from a stored library. It learns the patterns in a large amount of training data, then samples from those patterns to build something new — piece by piece. Step through the idea.

WalkthroughStep or run the flow

Learn patterns

Train on lots of data. The model studies many examples and learns the regularities — which words, pixels, or sounds tend to go together.

Get a prompt

You ask for something. A prompt sets the direction — a topic, a style, a description of what you want.

Sample a piece

Produce the next piece. The model picks a likely next element — a word, a patch of image, a sound — guided by the patterns it learned.

Repeat

Keep going. Each new piece feeds the next decision, so the output stays coherent as it grows.

Assemble

Deliver the result. The pieces combine into a finished, original output — similar in style to the training data, but not a copy of any one example.

Worth knowing: generative AI is powerful but not infallible. It can produce output that sounds confident yet is wrong, or that closely echoes its training data. Treat results as a strong first draft — review and verify before relying on them, especially for anything important.

04Check your understanding

TJS Quiz

Certificate of Completion

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A self-assessment summary recognizing completion of an educational module — not a professional certification.

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05Take it with you & go deeper

"Generative AI in 5 minutes" — one-page summary

The whole module distilled to a printable cheat-sheet.

▸ Already on the site — go deeper

Live article

What is generative AI? A beginner's guide

The full plain-language walkthrough — the natural next read.

Read →

Coming soon

How diffusion models work

From random noise to a finished image — the method behind most image generators.

In the pipeline

Coming soon

Generative AI use cases

Where it helps day to day — writing, design, support, coding, and more.

In the pipeline

→Continue learning

Deep diveGenerative AI for beginners →A fuller beginner guide Related lessonDiffusion & image generation →How generative images actually work

Sources & review

Published by Tech Jacks Solutions · Reviewed June 2026. This lesson explains established concepts and is grounded in the references below; figures shown in the interactives are illustrative and labelled as such.

Deep Learning (Ch. 20 — Deep Generative Models) — Goodfellow, Bengio & Courville — free online
What is generative AI? — IBM
Generative Adversarial Networks (GANs) — Goodfellow et al. (2014)
What is generative AI? — Google Cloud
Denoising Diffusion Probabilistic Models (DDPM) — Ho, Jain & Abbeel (2020)
Attention Is All You Need — Vaswani et al. (2017)
AI Risk Management Framework (AI RMF 1.0) — NIST
On the Dangers of Stochastic Parrots — Bender, Gebru et al. (2021)

Generative AI — in 5 minutes

Tech Jacks Solutions · AI Knowledge Hub · educational summary

Discriminative vs generative

Discriminative AI tells things apart — it predicts a label from an input (spam-or-not, cat-or-dog). Generative AI creates new content (text, images, audio, code) that resembles its training data. Both learn from data; they use it for opposite purposes.

How it generates

It learns the patterns / distribution of a large amount of training data, then samples from those patterns to produce something new — built piece by piece, not copied from a stored library.

Modalities

Text — LLMs (sentences, summaries, answers). Image — often diffusion models (a picture from a description). Audio — voice, speech, and music synthesis. Code — functions, fixes, and explanations across languages.

Use it wisely

Powerful but fallible: output can be plausible-but-wrong or derivative. Treat results as a draft — review and verify before relying on them, especially for anything important.

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Contacts